Agentic AI is breaking the assumptions legacy databases were built on. One user action now triggers thousands of agent instances. Context has to branch per agent in milliseconds. Vectors, transactions, and analytics need to live in the same place. Fragile multi-database stacks cannot keep up, and the engineering teams holding them together are paying the cost in reliability, operational overhead, and time to market.
It is time to rethink the database layer for how software actually gets built now.

Join us at TiDB SCaiLE Europe 2026, the premier European event for teams building the data infrastructure behind agentic AI. Come see what becomes possible when the database is built for agent behavior, not retrofitted for it.

Thursday, 4th June, 2026

09:00 AM – 07:30 PM

Epicenter Stockholm

Screenshot 2026-04-16 at 9.02.28 AM
What to Expect?

Real-World Success Stories

Hear from customers and users who have harnessed the power of TiDB to drive business growth and innovation

Expert Led Deep Dives

Explore strategies for scaling your data infrastructure to meet tomorrow’s demands while accelerating AI app builds and time-to-market

Networking Opportunities

Connect with TiDB users, engineers, and peers to build relationships and learn from their experiences

Who Should Attend?
  • Developers and engineers building applications on distributed or cloud-native databases
  • Data and platform architects designing infrastructure for scale and resilience
  • Engineering leaders evaluating modern data strategies for AI-native workloads
  • Technical decision-makers exploring alternatives to legacy database infrastructure

Built Something Worth Sharing?

Share your ideas with a global audience of engineers and data leaders at TiDB ScaIL Europe 2026. Submit your proposal by May 18th.

Submit Your Proposal
Agenda
10:00 - 11:00
Keynote
Ed Huang
Ed Huang
CTO, TiDB
11:00 - 11:30
TiDB in Practice: Scaling Databases Without Losing Sleep
Tarmo-Kople
Tarmo Kople
IT Infrastructure Architect, LHV Bank
Many teams discover TiDB through its promises of scalability and HTAP. But real-world adoption takes more than a strong benchmark or a catchy acronym. In this session, Tarmo Kople, IT Infrastructure Architect at LHV Bank (and formerly Bolt’s DB Team Lead), shares his journey deploying and evaluating TiDB across two very different organizations: from high-growth startups to regulated banking environments.
11:30 - 12:00
Breaking Up with MySQL: How Bolt Rebuilt for 100 Million Users on TiDB
Leandro Morgado
Leandro Morgado
Senior Database Reliability Engineer, Bolt
Bolt grew 400% post-pandemic to become Europe's fastest-growing mobility company, serving 100 million users across 500+ cities. But their MySQL infrastructure couldn't keep up. Adding a single column to a loaded 1TB table took up to a week, and managing hundreds of schemas across thousands of microservices had become an operational nightmare. In this session, Bolt's database reliability team shares how they evaluated alternatives, why TiDB's MySQL compatibility and horizontal scalability won the decision, and how they migrated their most critical workloads to seven TiDB clusters on AWS while achieving 3:1 data compression and five-nines reliability in production.
12:00 - 13:00
Lunch
13:00 am - 13:30 pm
Vector Search Meets Distributed SQL: Why Agentic AI Doesn't Need Another Database
Mattias Jonsson
Mattias Jonsson
Principal Software Engineer, TiDB
Most agentic AI architectures bolt a vector database onto an existing stack, adding another system to manage, another sync pipeline to maintain, and another failure point to monitor. But what if your SQL database already spoke vectors natively? In this session, Mattias Jonsson explores how built-in vector search and AI-native capabilities in distributed SQL eliminate the need for a separate vector database. The session covers how vector embeddings, semantic search, and agent memory patterns work alongside transactional and analytical workloads in a single query layer, what that means for developers building agentic applications today, and where the convergence of SQL and vector is headed next.
13:30 - 14:00
Migrating from MySQL to Distributed SQL: What Changes, What Doesn't, and What Breaks
Daniel-bio
Daniel Van Eeden
Technical Solutions Engineer, TiDB
Most teams evaluating distributed SQL start with one question: How compatible is it with what we already run? The answer is more nuanced than any compatibility matrix suggests. This session digs into what actually happens when MySQL workloads move to distributed SQL: the queries that run identically, the assumptions that quietly break, the transaction semantics that shift, and the Raft consensus and LSM tree internals that explain why. Drawing from real-world production migrations across Europe, Daniel van Eeden uncovers the patterns that transfer cleanly, the gotchas that don't surface until production, and the framework you need to evaluate what your workloads actually require.
14:00- 14:15
Break
14:15 - 15:15
Why One of Europe's Largest Energy Companies Bet on TiDB to Replace Oracle
Engie
-
Engie operates across 70+ countries and generates nearly €94 billion in annual revenue, but its reliance on Oracle databases was driving escalating costs and limiting flexibility at a time when the business needed to scale. In this session, Engie's engineering team shares how they evaluated MySQL, MariaDB, CockroachDB, and TiDB as open-source alternatives, what trust and licensing concerns eliminated the other candidates, and how a trial deployment quickly moved into production. From there, TiDB expanded into machine learning and billing workloads, proving the reliability and scalability needed to support one of the world's largest energy companies as it moves away from proprietary infrastructure.
15:15 - 15:30
Break
15:30 - 16:00
Dify
16:00 - 16:30
When EXPLAIN Isn't Enough: Visualising Vector Search for Engineering and Product Teams
Simon Hearne
Founding Solutions Architect, Zilliz
SQL makes sense. But when it breaks, you reach for EXPLAIN. Vector search offers no such comfort. Multi-thousand-dimension embeddings, approximate nearest-neighbour indexes, and quantisation tradeoffs make it hard to know what your system is doing, and harder still to diagnose when results quietly degrade. Through interactive visualisations, Simon Hearne shows what embeddings look like in high-dimensional space, what quantisation does to your recall, and how to catch retrieval failures before your agents do. You'll leave with a sharper mental model and a diagnostic toolkit for the production problems hardest to see.
16:30 - 16:45
Break
16:45 - 17:30
Panel/Fireside Chat